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CEC
2008
IEEE
15 years 4 months ago
Learning what to ignore: Memetic climbing in topology and weight space
— We present the memetic climber, a simple search algorithm that learns topology and weights of neural networks on different time scales. When applied to the problem of learning ...
Julian Togelius, Faustino J. Gomez, Jürgen Sc...
GECCO
2003
Springer
182views Optimization» more  GECCO 2003»
15 years 3 months ago
Spatial Operators for Evolving Dynamic Bayesian Networks from Spatio-temporal Data
Learning Bayesian networks from data has been studied extensively in the evolutionary algorithm communities [Larranaga96, Wong99]. We have previously explored extending some of the...
Allan Tucker, Xiaohui Liu, David Garway-Heath
CCE
2008
14 years 9 months ago
Differential recurrent neural network based predictive control
An efficient algorithm to train general differential recurrent neural networks is proposed. The trained network can be directly used as the internal model of a predictive controll...
R. K. Al Seyab, Yi Cao
EVOW
2008
Springer
14 years 11 months ago
Artificial Creatures for Object Tracking and Segmentation
We present a study on the use of soft computing techniques for object tracking/segmentation in surveillance video clips. A number of artificial creatures, conceptually, "inhab...
Luca Mussi, Stefano Cagnoni
GECCO
2005
Springer
131views Optimization» more  GECCO 2005»
15 years 3 months ago
Statistical analysis of heuristics for evolving sorting networks
Designing efficient sorting networks has been a challenging combinatorial optimization problem since the early 1960’s. The application of evolutionary computing to this problem ...
Lee K. Graham, Hassan Masum, Franz Oppacher